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3.
Whatisthe Web of Linked Data?<br />An extension of the current Web…<br />… where information and services are given well-defined and explicitly represented meaning, …<br />… so that it can be shared and used by humans and machines, ...<br />... better enabling them to work in cooperation<br />How?<br />Promoting information exchange by tagging web content with machineprocessable descriptions of its meaning. <br />And technologies and infrastructure to do this<br />And clear principles on how to publish data<br />data<br />

4.
What is Linked Data?<br />Linked Data is a term used to describe a recommended best practice for exposing, sharing, and connecting pieces of data, information, and knowledge on the Semantic Web using URIs and RDF.<br />Part of the Semantic Web<br />Exposing, sharing and connecting data<br />Technologies: URIs and RDF (although others are also important)<br />

5.
The fourprinciples (Tim Berners Lee, 2006)<br />Use URIs as names for things <br />Use HTTP URIs so that people can look up those names. <br />When someone looks up a URI, provide useful information, using the standards (RDF*, SPARQL) <br />Include links to other URIs, so that they can discover more things. <br />http://www.w3.org/DesignIssues/LinkedData.html<br />5<br />http://www.ted.com/talks/tim_berners_lee_on_the_next_web.html<br />

26.
URIs (Universal-UniformResourceIdentifer)<br />Two types of identifiers can be used to identify Linked Data resources<br />URIRefs(Unique Resource IdentifiersReferences)<br />A URI and an optional FragmentIdentifier separated from the URI by the hash symbol ‘#’<br />http://www.ontology.org/people#Person<br />people:Person<br />Plain URIs can also be used, as in FOAF:<br />http://xmlns.com/foaf/0.1/Person<br />17<br />

66.
RDF Containers<br />There is often the need to describe groups of things<br />A book was created by several authors<br />A lesson is taught by several persons<br />etc.<br />RDF provides a container vocabulary<br />rdf:Bag  A group of resources or literals, possibly including duplicate members, where the order of members is not significant<br />rdf:Seq  A group of resources or literals, possibly including duplicate members, where the order of members is significant<br />rdf:Alt  A group of resources or literals that are alternatives (typically for a single value of a property)<br />rdf:type<br />person:hasEmailAddress<br />oeg:Oscar<br />rdf:Seq<br />rdf:_2<br />rdf:_1<br />“oscar.corcho@upm.es”<br />“ocorcho@fi.upm.es”<br />49<br />

68.
Main value of a structured value<br />Sometimes one of the values of a structured value is the main one<br />The weight of an item is 2.4 kilograms <br />The most important value is 2.4, which is expressed with rdf:value<br />Scarcely used<br />product:hasWeight<br />product:Item1<br />rdf:value<br />units:hasWeightUnit<br />units:kilogram<br />2.4<br />51<br />

70.
RDF inference. Graph matching techniques <br />RDF inference is based on graph matching techniques<br />Basically, the RDF inference process consists of the following steps:<br />Transform an RDF query into a template graph that has to be matched against the RDF graph<br />It contains constant and variable nodes, and constant and variable edges between nodes<br />Match against the RDF graph, taking into account constant nodes and edges<br />Provide a solution for variable nodes and edges<br />53<br />

85.
Exercise 2.a. Create a graph from a file<br />Open the files StickyNote.rdf and StickyNote.rdfs<br />Create the corresponding graph from them<br />Compare your graph with those of your colleagues<br />64<br />

92.
RDF(S) limitations<br />RDFS too weak to describe resources in sufficient detail<br />No localised range and domain constraints<br />Can’t say that the range of hasChild is person when applied to persons and elephant when applied to elephants<br />No existence/cardinality constraints<br />Can’t say that all instances of person have a mother that is also a person, or that persons have exactly 2 parents<br />No boolean operators<br />Can’t say or, not, etc.<br />No transitive, inverse or symmetrical properties<br />Can’t say that isPartOf is a transitive property, that hasPart is the inverse of isPartOf or that touches is symmetrical<br />Difficult to provide reasoning support<br />No “native” reasoners for non-standard semantics<br />May be possible to reason via FOL axiomatisation<br />71<br />

109.
Query types<br />Selection and extraction<br />“Select all the essays, together with their authors and their authors’ names”<br />“Select everything that is related to the book ‘Bellum Civille’” <br />Reduction: we specify what it should not be returned<br />“Select everything except for the ontological information and the book translators”<br />Restructuring: the original structure is changed in the final result<br />“Invert the relationship ‘author’ by ‘is author of’”<br />Aggregation<br />“Return all the essays together with the mean number of authors per essay”<br />Combination and inferences<br />“Combine the information of a book called ‘La guerra civil’ and whose author is Julius Caesar with the book whose identifier is ‘Bellum Civille’”<br />“Select all the essays, together with its authors and author names”, including also the instances of the subclasses of Essay<br />“Obtain the relationship ‘coauthor’ among persons who have written the same book”<br />85<br />

118.
One of the result forms is applied: SELECT, CONSTRUCT, DESCRIBE, ASK</li></ul>91<br />

119.
Graph patterns<br />Basic Graph Patterns, where a set of triple patterns must match<br />Group Graph Pattern, where a set of graph patterns must all match<br />Optional Graph patterns, where additional patterns may extend the solution<br />Alternative Graph Pattern, where two or more possible patterns are tried<br />Patterns on Named Graphs, where patterns are matched against named graphs<br />92<br />

152.
OWL 2 (II). Three new profiles<br />OWL2 EL<br />Ontologies that define very large numbers of classes and/or properties, <br />Ontology consistency, class expression subsumption, and instance checking can be decided in polynomial time. <br />OWL2 QL<br />Sound and complete query answering is in LOGSPACE (more precisely, in AC0) with respect to the size of the data (assertions),<br />Provides many of the main features necessary to express conceptual models (UML class diagrams and ER diagrams). <br />It contains the intersection of RDFS and OWL 2 DL.<br />OWL2 RL<br />Inspired by Description Logic Programs and pD*. <br />Syntactic subset of OWL 2 which is amenable to implementation using rule-based technologies, and presenting a partial axiomatization of the OWL 2 RDF-Based Semantics in the form of first-order implications that can be used as the basis for such an implementation. <br />Scalable reasoning without sacrificing too much expressive power. <br />Designed for<br />OWL applications trading the full expressivity of the language for efficiency, <br />RDF(S) applications that need some added expressivity from OWL 2. <br />

159.
GeoLinkedData<br />It is an open initiative whose aim is to enrich the Web of Data with Spanish geospatial data.<br />This initiative has started off by publishing diverse information sources, such as National Geographic Institute of Spain (IGN-E) and National Statistics Institute (INE)<br />http://geo.linkeddata.es<br />

199.
5. Data cleansing<br />Identification<br />of the data sources<br />Lack of documentation of the IGN datasets<br />Broken links: Spain, IGN resources<br />Lack of documentation of theontology<br />Missingenglish and spanishlabels<br />Building a spanish ontology and importing some concepts of other ontology (in English):<br />Importing the English ontology. Add annotations like a Spanish label to them.<br />Importing the English ontology, creating new concepts and properties with a Spanish name and map those to the English equivalents.<br />Re-declaring the terms of the English ontology that we need (using the same URI as in the English ontology), and adding a Spanish label.<br />Creating your own class and properties that model the same things as the English ontology. <br />Vocabulary<br />development<br />Generation<br />of the RDF Data<br />Publication<br />of the RDF data <br />Data cleansing<br />Linking <br />the RDF data<br />Enable effective <br />discovery<br />

209.
Future Work<br />Generate more datasets from other domains, e.g. universities in Spain.<br />Identify more links to DBPedia and Geonames.<br />Cover complex geometrical information, i.e. not only Point and LineString-like data; we will also treat information representation through polygons.<br />

216.
Population example (II)<br />Population example (II)<br />The Operation element defines a transformation based on a regular expression to be applied to the database column for extracting property values<br />

217.
For concepts...<br />One or more concepts can be extracted from a single data field (not in 1NF).<br />A view maps exactly one concept in the ontology.<br />For attributes...<br />A column in a database view maps directly an attribute or a relation.<br />A subset of the columns in the view map a concept in the ontology.<br />A subset (selection) of the records of a database view map a concept in the ontology.<br />A column in a database view maps an attribute or a relation after some transformation.<br />A subset of the records of a database view map a concept in the onto. but the selection cannot be made using SQL.<br />A set of columns in a database view map an attribute or a relation.<br />R2O (Relational-to-Ontology) Language<br />